class_docstr: A class that computes different kinds of        :term:`functional connectivity` matrices.
  
  .. versionadded:: 0.2
  
  Parameters
  ----------
  cov_estimator : estimator object,                     default=LedoitWolf(store_precision=False)
      The covariance estimator.
      This implies that correlations are slightly shrunk
      towards zero compared to a maximum-likelihood estimate
  
  kind : {"covariance", "correlation", "partial correlation",            "tangent", "precision"}, default='covariance'
      The matrix kind.
      For the use of "tangent" see :footcite:t:`Varoquaux2010b`.
  
  vectorize : bool, default=False
      If True, connectivity matrices are reshaped into 1D arrays and only
      their flattened lower triangular parts are returned.
  
  discard_diagonal : bool, default=False
      If True, vectorized connectivity coefficients do not include the
      matrices diagonal elements. Used only when vectorize is set to True.
  
  %(standardize)s
  
      .. note::
  
          Added to control passing value to `standardize` of ``signal.clean``
          to call new behavior since passing "zscore" or True (default) is
          deprecated. This parameter will be deprecated in version 0.13 and
          removed in version 0.15.
  
  Attributes
  ----------
  cov_estimator_ : estimator object
      A new covariance estimator with the same parameters as cov_estimator.
  
  mean_ : numpy.ndarray
      The mean connectivity matrix across subjects. For 'tangent' kind,
      it is the geometric mean of covariances (a group covariance
      matrix that captures information from both correlation and partial
      correlation matrices). For other values for "kind", it is the
      mean of the corresponding matrices
  
  whitening_ : numpy.ndarray
      The inverted square-rooted geometric mean of the covariance matrices.
  
  References
  ----------
  .. footbibliography::
docstr: Apply transform to covariances matrices to get the connectivity         matrices for the chosen kind.
  
  Parameters
  ----------
  X : list of n_subjects numpy.ndarray with shapes             (n_samples, n_features)
      The input subjects time series. The number of samples may differ
      from one subject to another.
  
  confounds : numpy.ndarray with shape (n_samples) or                     (n_samples, n_confounds), optional
      Confounds to be cleaned on the vectorized matrices. Only takes
      into effect when vetorize=True.
      This parameter is passed to signal.clean. Please see the related
      documentation for details.
  
  Returns
  -------
  output : numpy.ndarray, shape (n_subjects, n_features, n_features) or             (n_subjects, n_features * (n_features + 1) / 2) if vectorize             is set to True.
      The transformed individual connectivities, as matrices or vectors.
      Vectors are cleaned when vectorize=True and confounds are provided.
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code: def transform(self, X, confounds=None):
      """Apply transform to covariances matrices to get the connectivity \
          matrices for the chosen kind.
  
          Parameters
          ----------
          X : list of n_subjects numpy.ndarray with shapes \
              (n_samples, n_features)
              The input subjects time series. The number of samples may differ
              from one subject to another.
  
          confounds : numpy.ndarray with shape (n_samples) or \
                      (n_samples, n_confounds), optional
              Confounds to be cleaned on the vectorized matrices. Only takes
              into effect when vetorize=True.
              This parameter is passed to signal.clean. Please see the related
              documentation for details.
  
          Returns
          -------
          output : numpy.ndarray, shape (n_subjects, n_features, n_features) or \
              (n_subjects, n_features * (n_features + 1) / 2) if vectorize \
              is set to True.
              The transformed individual connectivities, as matrices or vectors.
              Vectors are cleaned when vectorize=True and confounds are provided.
  
          """
      self._check_fitted()
      return self._fit_transform(
          X, do_transform=True, confounds=confounds
      )
  
imports: import warnings
  from math import floor, sqrt
  import numpy
  from scipy import linalg
  from sklearn.base import BaseEstimator, TransformerMixin, clone
  from sklearn.covariance import LedoitWolf
  from nilearn._utils.docs import fill_doc
  from ..None import signal
  from .._utils.extmath import is_spd
label_desc: Use image node_id 6 for calling the ConnectivityMeasure obj's underlying member method code with example usage: obj.transform(X, confounds) and returns: self